This research investigates analytical and quantitative methods for simulating elevator optimizations. To maximize overall elevator usage, we concentrate on creating a multiple-user positive-sum system that is inspired by agent-based game theory. We define and create basic "Dumbwaiter" models by attempting both the Spatial Process Approach and the Gibbs Random Field Approach. These two mathematical techniques approach the problem from different points of view: the spatial process can give an analytical solution in continuous space and the Gibbs Random Field provides a discrete framework to flexibly model the problem on a computer. Starting from the simplest case, we target the assumptions to provide concrete solutions to the models and develop a "Multi-Dumbwaiter System". This paper examines, evaluates, and proves the ultimate success of such implemented strategies to design the basic elevator's optimal policy; consequently, not only do we believe in the results' practicality for industry, but also their potential for application.

翻译：这项研究调查了模拟电梯优化的分析和定量方法。 为了最大限度地提高电梯的总体使用率, 我们专注于创建一个由代理游戏理论启发的多用户正和系统。 我们通过尝试空间过程方法和Gibbs随机场方法来定义和创建基本的“ Dumbwaiter ” 模型。 这两种数学技术从不同的角度处理问题: 空间过程可以在连续空间中提供分析解决方案, Gibbs随机场提供了一个在计算机上灵活模拟问题的离散框架。 从最简单的例子开始, 我们设定的假设目标是为模型提供具体解决方案并开发“ Multi- Dumbwaiter 系统 ” 。 本文考察、 评估并证明这些已实施的战略最终成功设计了基本电梯的最佳政策; 因此, 我们不仅相信结果对产业的实际意义,而且相信其应用潜力。